Sample portfolio recommendation
ZZAlpha LTD. provides daily recommendations of stocks that are likely to go up (or down - short) in value over the next four to ten days. (We measure results assuming a hold of 5 trading days.) We use an objective, proprietary and effective machine learning technique to create the recommendations. Our recommendation portfolios encompass diverse liquidity, economic sector, and capitalization groupings, and are available for both long and short positions.
The daily emailed investment recommendation newsletter has this typical form (sample):
Note: ZZAlpha LTD. does not provide individualized investment advice, does not handle client funds, and does not buy or sell securities.
Proof Protocol for a Machine Learning Technique Making
Longitudinal Predictions in Dynamic Contexts. Presented at
ACM Knowledge Discovery and Datamining Conference, August
2015, Sidney, Australia. Kevin B. Pratt, Chief Scientist,
Presentation slide deck here.
Abstract: We demonstrate a protocol for proving strongly that a black-box machine learning technique robustly predicts the future in dynamic, indefinite contexts. We propose necessary components of the proof protocol and demonstrate results visualizations to support evaluation of the proof components. Components include contemporaneously verifiable discrete predictions, deterministic computability of longitudinal predictions, imposition of realistic costs and domain constraints, exposure to diverse contexts, statistically significant excess benefits relative to a priori benchmarks and Monte Carlo trials, insignificant decay of excess benefits, pathology detection and an extended real-time trial “in the wild.” We apply the protocol to a big data machine learning technique deployed since 2011 that finds persistent, exploitable opportunities in many of 41 segments of US financial markets, the existence of which opportunities substantially contradict the Efficient Market Hypothesis.
Effective Market Timing: Recent successes
in five key dimensions using one-week market forecasts
produced by machine learning White Paper-Sept.
24, 2011, updated March 24, 2012 . Presented at
Predictive Analytics World Conference, San Francisco,
CA, Feb. 2012. Kevin B. Pratt, Chief Scientist,
Abstract: We demonstrate that a machine learning technique predicts relative future price in four key dimensions and an economic core of the US equities market. The price forecasts enable effective market timing selections among: a) equities vs. bonds, b) growth vs. value vs. bonds, c) large cap vs. small cap vs. bonds, d) among twelve economic sectors (including bonds) and e) economic core materials and energy sectors (and bonds). Market timing using these one-week forecasts support annualized returns over 12% for study period Jan. 2007 through Dec. 2011 using unleveraged long positions in large, well-known ETFs. The returns exceed benchmarks in the study period. The market timing also reduces risk relative to benchmarks. Using large Monte Carlo simulations, we confirm that statistical confidence in the results from the market timing recommendations exceeds three sigma (over 99.7%). full text available here
ZZAlpha Portfolios: Their performance, Risk and
Usability White Paper-June 21, 2011. Kevin B.
Pratt, Chief Scientist, ZZAlpha LTD.
Abstract: An introduction to the ZZAlpha Portfolios with description of sample portfolios, performance evaluation methodology, consistency of returns (2005-2010), performance, risk profiles, practical tradability, and comments on long-short equity hedging with the portfolios. Appendices include overview of the machine learning methodology, performance recording methodology, Monte Carlo trials and statistical significance, and comments on misleading risk-adjusted return statistics and bell curves.
The Efficient Market Hypothesis is false: proof
beyond a reasonable doubt (journal submission
pending additional certified results). Kevin B.
Pratt, Chief Scientist, ZZAlpha LTD.
Tentative Abstract: The Efficient Market Hypothesis (EMH) posits that traders in public equities markets rapidly incorporate public information in their buying and selling such that the current price accurately reflects all available knowledge. Recent observers have asserted that even if the equities markets pre-2000 were inefficient, the high information velocity of the internet, electronic real-time publication, and high frequency algorithmic trading have now reduced the life of any inefficiencies to seconds. We demonstrate that in 2005-2014 the market was not efficient by using a machine learning technique to consistently identify, with high confidence, large populations of equities whose prices substantially deviated, both up and down, over a one-week period, from the population means and relevant benchmarks. We show consistent results across liquidity, capitalization, sector and industry segments, as well as within recognized groupings such as the Standard and Poor's 500 list.
October 5, 2011 Machine learning answers: Get in or out of the stock market? available here
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